A NOVEL FRAMEWORK FOR PREDICTING HEAVY METAL CONTAMINATION RISKS IN SURIMI-BASED PRODUCTS, COMBINING BIOACCUMULATION DYNAMICS WITH BLOCKCHAIN-ENABLED SUPPLY CHAIN TRANSPARENCY IN CHAOSHAN

Abstract

This study aimed to develop an integrated predictive framework that bridges environmental science, food processing engineering, and information technology to enable proactive contamination risk management in processed aquatic products.

A tripartite data integration strategy was employed, synthesizing governmental environmental monitoring records, industrial production databases, and peer-reviewed biokinetic parameters spanning 2018–2023. Mathematical models were constructed to characterize two distinct phases: (1) a first-order compartmental model describing pollutant uptake dynamics in marine fish species, and (2) a mass-balance transformation model quantifying contaminant fate during thermal processing. These quantitative outputs were synthesized into a weighted composite risk metric using multi-criteria decision analysis. The framework was operationalized through a permissioned distributed ledger system incorporating automated validation protocols. Analysis revealed substantial temporal displacement between environmental contamination events and biological tissue accumulation, with cross-correlation indicating approximately 3.5-month lag periods. Critically, processing effects demonstrated contaminant-specific divergence: water-soluble ionic metals exhibited 29% reduction through aqueous leaching, whereas protein-associated organic compounds showed 12% concentration increase due to moisture loss during thermal treatment. The integrated risk metric demonstrated 89.2% predictive accuracy against historical quality control outcomes, while the distributed ledger architecture reduced incident investigation duration from 48–72 hours to under 3 seconds.These findings challenge the prevailing assumption that food processing uniformly reduces contamination levels and demonstrate that static sampling protocols inadequately characterize risk in processed aquatic products. The proposed predictive governance architecture offers a paradigm shift from reactive inspection to anticipatory intervention, providing scalable infrastructure for modernizing food safety assurance in traditional food industries while maintaining cultural heritage product integrity.